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Remote Sens. 2013, 5(12), 6346-6360;

An Improved Image Fusion Approach Based on Enhanced Spatial and Temporal the Adaptive Reflectance Fusion Model

State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Beijing 100101, China
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, NT, Hong Kong 8520, China
Department of Geography, The Ohio State University, Columbus, OH 43210, USA
College of Forestry, Oregon State University, 231 Peavy Hall, Corvallis, OR 97331, USA
Author to whom correspondence should be addressed.
Received: 1 August 2013 / Revised: 8 November 2013 / Accepted: 11 November 2013 / Published: 26 November 2013
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High spatiotemporal resolution satellite imagery is useful for natural resource management and monitoring for land-use and land-cover change and ecosystem dynamics. However, acquisitions from a single satellite can be limited, due to trade-offs in either spatial or temporal resolution. The spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM (ESTARFM) were developed to produce new images with high spatial and high temporal resolution using images from multiple sources. Nonetheless, there were some shortcomings in these models, especially for the procedure of searching spectrally similar neighbor pixels in the models. In order to improve these models’ capacity and accuracy, we developed a modified version of ESTARFM (mESTARFM) and tested the performance of two approaches (ESTARFM and mESTARFM) in three study areas located in Canada and China at different time intervals. The results show that mESTARFM improved the accuracy of the simulated reflectance at fine resolution to some extent. View Full-Text
Keywords: image fusion; reflectance; Landsat; MODIS image fusion; reflectance; Landsat; MODIS
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Fu, D.; Chen, B.; Wang, J.; Zhu, X.; Hilker, T. An Improved Image Fusion Approach Based on Enhanced Spatial and Temporal the Adaptive Reflectance Fusion Model. Remote Sens. 2013, 5, 6346-6360.

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